{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def createCmap(rgbfile,name='example',n=1):\n",
    "    import numpy as np \n",
    "    from matplotlib.colors import ListedColormap\n",
    "    with open('./GreenMagenta16.rgb','r') as f:\n",
    "        ca=f.readlines()\n",
    "    for j,i in enumerate(ca) :\n",
    "        pan=('r' in i,'g' in i,'b' in i)\n",
    "        if all(pan):\n",
    "            skiprows=j+1\n",
    "    colors=np.loadtxt(rgbfile,skiprows=skiprows,dtype=np.int64)\n",
    "    if n==1:\n",
    "        n=len(colors)\n",
    "    colorlist=[]\n",
    "    for i in range(len(colors)):\n",
    "        colorlist.append(tuple(colors[i,:]/256))\n",
    "    cm = ListedColormap(\n",
    "        colorlist,  \n",
    "        name=name,\n",
    "        N = n)\n",
    "    return cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/html": [
       "<div style=\"vertical-align: middle;\"><strong>example</strong> </div><div class=\"cmap\"><img alt=\"example colormap\" title=\"example\" style=\"border: 1px solid #555;\" src=\"\"></div><div style=\"vertical-align: middle; max-width: 514px; display: flex; justify-content: space-between;\"><div style=\"float: left;\"><div title=\"#000033ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #000033ff;\"></div> under</div><div style=\"margin: 0 auto; display: inline-block;\">bad <div title=\"#00000000\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #00000000;\"></div></div><div style=\"float: right;\">over <div title=\"#fefefeff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #fefefeff;\"></div></div>"
      ],
      "text/plain": [
       "<matplotlib.colors.ListedColormap at 0x1af6cc1cf10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm=createCmap('./BkBlAqGrYeOrReViWh200.rgb')\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.69756782, 0.03846161, 0.71493861, 0.70358966, 0.22171765],\n",
       "       [0.68463821, 0.95130611, 0.16895316, 0.78818989, 0.22409867],\n",
       "       [0.21977127, 0.1703277 , 0.13821792, 0.01327664, 0.56539029],\n",
       "       [0.79191626, 0.93117618, 0.27511299, 0.58225316, 0.27636364],\n",
       "       [0.62535745, 0.77429273, 0.61569543, 0.30875876, 0.69612379]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=np.random.rand(5,5)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cb=plt.contourf(range(5),range(5),x,levels=16,cmap=cm)\n",
    "plt.colorbar(cb)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.4 ('py310')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "1af7d1ae3c55d82bbda2a5569c7a9f2f2d33ff4c43dd1c666120ac4867346c8c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
